Used data in this analysis

Specifically, in this experiment set, known experiment labels are:

  • [C] GSM2858677
  • [C] GSM2858678
  • [C] GSM2858679
  • [C] GSM2858681
  • [C] GSM2858684
  • [C] GSM2858685
  • [C] GSM2858686
  • [C] GSM2858687
  • [C] GSM2858688
  • [C] GSM2858689
  • [C] GSM2858691
  • [C] GSM2858692
  • [C] GSM2858693
  • [C] GSM2858694
  • [C] GSM2858695
  • [C] GSM2858696
  • [C] GSM2858698
  • [C] GSM2858699
  • [C] GSM2858700
  • [C] GSM2858701
  • [C] GSM2858703
  • [C] GSM2858705
  • [C] GSM2858708
  • [C] GSM2858709
  • [C] GSM2858710
  • [C] GSM2858713
  • [C] GSM2858714
  • [C] GSM2858715
  • [C] GSM2858717
  • [C] GSM2858720
  • [C] GSM2858721
  • [C] GSM2858723
  • [C] GSM2858724
  • [C] GSM2858725
  • [C] GSM2858726
  • [C] GSM2858727
  • [C] GSM2858733
  • [C] GSM2858737
  • [C] GSM2858738
  • [C] GSM2858739
  • [C] GSM2858740
  • [C] GSM2858741
  • [C] GSM2858742
  • [C] GSM2858745
  • [C] GSM2858746
  • [C] GSM2858747
  • [C] GSM2858748
  • [C] GSM2858749
  • [C] GSM2858750
  • [C] GSM2858751
  • [C] GSM2858753
  • [C] GSM2858755
  • [C] GSM2858756
  • [C] GSM2858758
  • [C] GSM2858759
  • [C] GSM2858761
  • [C] GSM2858763
  • [C] GSM2858764
  • [C] GSM2858766
  • [C] GSM2858767
  • [C] GSM2858768
  • [C] GSM2858769
  • [C] GSM2858771
  • [C] GSM2858774
  • [C] GSM2858776
  • [C] GSM2858779
  • [C] GSM2858780
  • [C] GSM2858781
  • [C] GSM2858782
  • [C] GSM2858783
  • [C] GSM2858786
  • [C] GSM2858787
  • [C] GSM2858792
  • [T] GSM2858680
  • [T] GSM2858682
  • [T] GSM2858683
  • [T] GSM2858690
  • [T] GSM2858697
  • [T] GSM2858702
  • [T] GSM2858704
  • [T] GSM2858706
  • [T] GSM2858707
  • [T] GSM2858711
  • [T] GSM2858712
  • [T] GSM2858716
  • [T] GSM2858718
  • [T] GSM2858719
  • [T] GSM2858722
  • [T] GSM2858728
  • [T] GSM2858729
  • [T] GSM2858730
  • [T] GSM2858731
  • [T] GSM2858732
  • [T] GSM2858734
  • [T] GSM2858735
  • [T] GSM2858736
  • [T] GSM2858743
  • [T] GSM2858744
  • [T] GSM2858752
  • [T] GSM2858754
  • [T] GSM2858757
  • [T] GSM2858760
  • [T] GSM2858762
  • [T] GSM2858765
  • [T] GSM2858770
  • [T] GSM2858772
  • [T] GSM2858773
  • [T] GSM2858775
  • [T] GSM2858777
  • [T] GSM2858778
  • [T] GSM2858784
  • [T] GSM2858785
  • [T] GSM2858788
  • [T] GSM2858789
  • [T] GSM2858790
  • [T] GSM2858791
  • [T] GSM2858793
  • [T] GSM2858794
  • [T] GSM2858795

General description

This report contains all the functional information that was requested by the options when functional_Hunter.R was executed. The functional categories can be:

  • KEGG pathways
  • GO:
    • Biological Process
    • Molecular Function
    • Cellular Component
  • Reactome pathways
  • Custom nomenclature

All the functional categories are computed with CluterProfiler and GO caterogires are computed also with TopGo. Some sections will not show if there are not sinficative results. Each category is analysed using Over representation analysis (ORA) and Gene Set Analysis (GSEA). The ORA method takes a group of significative DEGs (only DEGs, upregulated DEGs or downregulated DEGs) and performs a hypergeometric test for each term of the selected functional category. In the case of the GSEA method, all the genes are sorted by their fold-change and the algorithm scan which genes with similar fold-change shares a term of the selected functional category.

Statistics about input results obtained from DEGenes Expression Hunter are:

Gene_tag Genes
PREVALENT_DEG 22552

Overview of clusters

The following table shows the numbers of prevalent vs. non-prevalent genes found in each cluster (first two columns), followed by the number of significantly enriched categories found in each cluster for each of the annotation sources found using the ORA method (remaining columns).

## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html

The following modules were run: MF BP CCResults were found in at least one module for the following: MF BP CC

MF Over Representation Analysis

The ORA method takes a group of significative DEGs (only DEGs, upregulated DEGs or downregulated DEGs) and performs a hypergeometric test for each term of the selected functional category.

Dotplot

The most highly signficant categories in descending in categories of gene ratio, defined as the proportion of significant genes that are found in the functional category. The x-axis represents the gene ratio and the dot size the number of genes associated with the functional category.

Heatplot

Significant genes (x-axis) and the functional categories in which they appear.

Enrich Map plot

The top functional categories (nodes), connected if they share genes. Edge thickness represents the number of shared genes. Nodes size represents the number of significant genes within the category.

BP Over Representation Analysis

The ORA method takes a group of significative DEGs (only DEGs, upregulated DEGs or downregulated DEGs) and performs a hypergeometric test for each term of the selected functional category.

Dotplot

The most highly signficant categories in descending in categories of gene ratio, defined as the proportion of significant genes that are found in the functional category. The x-axis represents the gene ratio and the dot size the number of genes associated with the functional category.

Heatplot

Significant genes (x-axis) and the functional categories in which they appear.

Enrich Map plot

The top functional categories (nodes), connected if they share genes. Edge thickness represents the number of shared genes. Nodes size represents the number of significant genes within the category.

## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

CC Over Representation Analysis

The ORA method takes a group of significative DEGs (only DEGs, upregulated DEGs or downregulated DEGs) and performs a hypergeometric test for each term of the selected functional category.

Dotplot

The most highly signficant categories in descending in categories of gene ratio, defined as the proportion of significant genes that are found in the functional category. The x-axis represents the gene ratio and the dot size the number of genes associated with the functional category.

Heatplot

Significant genes (x-axis) and the functional categories in which they appear.

Enrich Map plot

The top functional categories (nodes), connected if they share genes. Edge thickness represents the number of shared genes. Nodes size represents the number of significant genes within the category.